Reproduce “Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Sichen Li, Jochem Snuverink, Fernando Perez-Cruz, Andreas Adelmann
The authors develop a classifier to forecast accelerator interlocks from single snapshots of 376 channels, comparing readings 0.2 seconds before interlocks with readings 10 seconds before. L1 logistic regression achieved an AUC of 0.99; at threshold 0.8, it identified 983 of 1,192 interlocks and produced 222 false alarms. The 5.45-minute daily saving estimate assumes an undemonstrated recovery action.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.